Relevance vector machine based infinite decision agent ensemble learning for credit risk analysis

Li, S; Tsang, IW; Chaudhari, NS

Relevance vector machine based infinite decision agent ensemble learning for credit risk analysis

Li, S Tsang, IW

Chaudhari, NS

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Publication Type:: Journal Article
Citation:: Expert Systems with Applications, 2012, 39 (5), pp. 4947 - 4953
Issue Date:: 2012-04-01

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Field	Value	Language
dc.contributor.author	Li, S	en_US
dc.contributor.author	Tsang, IW https://orcid.org/0000-0001-8095-4637	en_US
dc.contributor.author	Chaudhari, NS	en_US
dc.date.issued	2012-04-01	en_US
dc.identifier.citation	Expert Systems with Applications, 2012, 39 (5), pp. 4947 - 4953	en_US
dc.identifier.issn	0957-4174	en_US
dc.identifier.uri	http://hdl.handle.net/10453/114915
dc.description.abstract	In this paper, a relevance vector machine based infinite decision agent ensemble learning (RVM Ideal) system is proposed for the robust credit risk analysis. In the first level of our model, we adopt soft margin boosting to overcome overfitting. In the second level, the RVM algorithm is revised for boosting so that different RVM agents can be generated from the updated instance space of the data. In the third level, the perceptron Kernel is employed in RVM to simulate infinite subagents. Our system RVM Ideal also shares some good properties, such as good generalization performance, immunity to overfitting and predicting the distance to default. According to the experimental results, our proposed system can achieve better performance in term of sensitivity, specificity and overall accuracy. © 2011 Elsevier Ltd. All rights reserved.	en_US
dc.relation.ispartof	Expert Systems with Applications	en_US
dc.relation.isbasedon	10.1016/j.eswa.2011.10.022	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	Relevance vector machine based infinite decision agent ensemble learning for credit risk analysis	en_US
dc.type	Journal Article
utslib.citation.volume	5	en_US
utslib.citation.volume	39	en_US
utslib.for	080101 Adaptive Agents and Intelligent Robotics	en_US
utslib.for	080109 Pattern Recognition and Data Mining	en_US
utslib.for	01 Mathematical Sciences	en_US
utslib.for	08 Information and Computing Sciences	en_US
utslib.for	09 Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CAI - Centre for Artificial Intelligence
utslib.copyright.status	closed_access
pubs.issue	5	en_US
pubs.publication-status	Published	en_US
pubs.volume	39	en_US

Abstract:

In this paper, a relevance vector machine based infinite decision agent ensemble learning (RVM Ideal) system is proposed for the robust credit risk analysis. In the first level of our model, we adopt soft margin boosting to overcome overfitting. In the second level, the RVM algorithm is revised for boosting so that different RVM agents can be generated from the updated instance space of the data. In the third level, the perceptron Kernel is employed in RVM to simulate infinite subagents. Our system RVM Ideal also shares some good properties, such as good generalization performance, immunity to overfitting and predicting the distance to default. According to the experimental results, our proposed system can achieve better performance in term of sensitivity, specificity and overall accuracy. © 2011 Elsevier Ltd. All rights reserved.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/114915